Method of predicting a blood dilution risk value

ABSTRACT

The present invention provides for a clinical decision support system which makes use of a numerical model which dynamically describes a blood dilution of a blood circulation. Based on measured coagulation data and maybe other patient information, loss of hemostatic balance is predicted based on calculations of certain protein concentrations. Additionally, a calculation arrangement translates at least some of the calculated values of concentrations of human blood proteins into a risk value which risk value describes a risk of clotting and/or embolism and/or bleeding. A time development of that risk value is displayed to the user.

FIELD OF THE INVENTION

The present invention relates to clinical decision support systems. Indetail, the present invention relates to a method of predicting a blooddilution risk value of a first blood circulation, to a clinical decisionsupport system for predicting and displaying a blood dilution riskvalue, a program element for predicting and displaying a blood dilutionrisk value and a computer readable medium.

BACKGROUND OF THE INVENTION

Patients who undergo surgery experience blood loss and loweredconcentrations of blood clotting proteins due to reactions of thehemostatic system to the surgical cuts. Apart from this, the patient'sclotting system may be inhibited by Heparin-like compounds to preventembolism. The blood loss and lowering of coagulation proteinconcentrations is countered by transfusions of blood plasma, IV fluids,protein substitution solutions etc. The challenge of maintaining a safehemostatic balance in a patient undergoing surgery is a very difficultone, and multiple strategies exist to meet this challenge.

The patient's coagulation state is monitored during surgery throughtests like hematocrit measurements, blood pressure measurements andmultiple coagulation tests (e.g. thrombo-elastometry, INR, aPTT). Theamounts of (blood) products administered to the patient are of coursemonitored as well. In current practice the monitored values indicatewhen the patient is out of hemostatic balance (e.g. his clottingpotential has become dangerously low), upon which a countermeasure (e.g.administration of protein substitutes) is put into effect.

A downside to the way of working as described above is thatcountermeasures are only taken when the patient is already out ofbalance, and stopped only when the hemostatic balance starts to tip inthe other direction.

SUMMARY OF THE INVENTION

It may be seen as an object of the present invention to provide for animproved blood dilution analysis.

There may be a need to analyze blood loss and lowered concentrations ofblood clotting proteins and provide accurate countermeasures before theanalyzed blood circulation is out of balance. Furthermore, there may bea need to provide a user with the information when the countermeasureshave to be stopped and to provide the user with said information beforethe hemostatic balance of the analyzed blood circulation starts to tipin the other direction.

The present invention matches these needs.

The object of the present invention is solved by the subject-matter ofthe independent claims. Further embodiments and further advantages areincorporated in the dependent claims.

It should be noted that the embodiments of the invention described inthe following similarly pertain to the method, the system, to theprogram element, as well as to the computer readable medium. In otherwords, features that will be described with regard to the embodimentsrelating to a method of predicting blood dilution risk value of a firstblood circulation shall be understood to be comprised or implemented bythe corresponding system, the program element, and the computer readablemedium of the present invention, and vice versa. Especially, theclinical decision support system according to the present invention canbe configured in such a way that all the below described methodembodiments of the present invention can be carried out by said clinicaldecision support system.

According to an exemplary embodiment of the invention, a method ofpredicting blood dilution risk value of a first blood circulation ispresented. The method comprises the step of providing measuredcoagulation data describing a hemostatic situation of the first bloodcirculation at a first point in time and applying the measuredcoagulation data as an input for a numerical model. The numerical modelis defined to be a mathematical and dynamical representation of a blooddilution of the first blood circulation. In other words, the numericalmodel describes a blood dilution situation or a blood dilutiondevelopment of the surveyed or analyzed blood circulation. The methodfurther comprises the step of performing a simulation of a timedevelopment of the hemostatic situation by means of the numerical modeland based on the measured coagulation data used as an input for thenumerical model. Further, calculating values of concentrations of humanblood proteins as an output of the simulation is performed. Furthermore,translating at least some of the calculated values of concentrations ofhuman blood proteins into a risk value, which risk value describes arisk of clotting and/or embolism and/or bleeding of the first bloodcirculation is performed by the presented method.

In other words, the method is configured to provide for a calculatedrisk value based on measured coagulation data, and moreover the methodis additionally configured to provide for a predicted risk value basedon a simulation by the numerical model.

In other words, the numerical model used herein describes a situation inwhich a blood circulation undergoes blood dilution due to for exampleblood loss or lowered concentrations of blood clotting proteins becauseof reactions of the hemostatic system due to e.g. a cut.

Furthermore, the term “calculating protein concentrations” may also beunderstood in the context of the present invention as calculating aconcentration of a complex which is formed by at least two differentproteins or as calculating a mass-length ratio of a protein, like forexample a fibrin fiber.

The step of providing measured coagulation data may for example beembodied by entering a number of transfusion units, that have beensupplied to a blood circulation to a graphical user interface, or may beembodied as providing a measured test value of a test which was appliedto the blood circulation like e.g. an INR, aPTT, thrombo-elastometry(ampl) or thrombo-elastometry (lag time) test value to a graphical userinterface. Such a graphical user interface may be connected with orcomprised of a clinical decision support system which performs thepresented method. The user may submit the previously and exemplarilymentioned data to a calculation arrangement to allow to perform acorresponding simulation as described above and in the following.

Furthermore the step of “translating” may be performed based on giventranslation rules and may be performed with regard to a desired specificembodiment of a risk value, like for example a risk value whichindicates the speed of sealing of a hypothetical wound. This will beexplained in more detail in the following, e.g. with regard to FIG. 6.

Furthermore, the term “hemostatic situation” may be understood to beused synonymously to a blood dilution state of the blood circulation. Inother words the herein presented numerical model enables a user tocalculate and predict the present and future blood dilution states ofthe analyzed blood circulation and translates the latter into a riskvalue, i.e. a blood dilution risk value.

Furthermore the term “at least some of the calculated values” shall beunderstood in the context of the present invention that also only onevalue of concentration of a human blood protein can be translated ifdesired. However, a plurality of concentrations of human blood proteinsis comprised by said term.

Specifically the term “human blood protein” may be understood in thepresent invention to also comprise coagulation proteins.

In general, the risk value may be seen as a blood dilution risk value.The risk value may be embodied as a value ranging between 0 and 1 or maybe embodied as a displayed color that may have different nuances withrespect to the present underlying predicted risk. However, otherdifferent representations of the risk value shall be comprised in thescope of the present invention.

In this and every other exemplary embodiment the term “calculating” maybe understood as performing a simulation with a model e.g. with themathematical model described herein.

It may be of importance that the presented method for performing theprediction does not require any contact with the patient, as it ispurely based on a mathematical model.

In other words the presented method makes use of a computer model, basedon a biochemical model, which will be explained with certain embodimentshereinafter. The model may use biomedical knowledge and experiments, mayuse the measured monitored test values, i.e. the provided coagulationdata like e.g. the above presented number of transfusion units, andother patient information to predict a state of blood dilution thatinvolves a risk of clotting and/or embolism and/or bleeding before itoccurs. In other words, when transfusion is given, blood will bediluted. What is predicted by the present invention is whether a patientwill become at risk as a result of the dilution.

Such a predicted dangerous state of dilution may be displayed to theuser by the calculated and predicted blood dilution risk value, whichwas generated by the model based on the calculated and predicted bloodprotein concentrations. Furthermore, the method may comprise tocalculate the correspondingly expected effect of each availablecountermeasure. The solution may be offered to a user by a graphicaluser interface which interface may be linked to a hospital informationsystem. Therefore, the presented method provides for a continuousestimation of the patients stability, and if desired, an alarm when thepatient threatens to become unstable and may suggest for the optimalcountermeasure.

According to another exemplary embodiment of the present invention themethod comprises the steps of calculating a first predicted risk valuebased on an assumed first countermeasure, calculating a second predictedrisk value based on an assumed second countermeasure, comparing thefirst and second predicted risk value, and recommending thecountermeasure from the first and the second countermeasure whichprovides for the lower risk value.

According to another exemplary embodiment of the present invention, thecalculation of the values of the concentrations of the human bloodproteins is generating a predicted time development of saidconcentrations of human blood proteins as the output of the simulation.

In a further step a generated time development of said concentrationsmay be translated into a time development of the risk value, which mayadditionally be graphically displayed to a user. In other words, thepresented embodiment may use predicted time series of coagulationproteins for the translation into a risk value.

According to another exemplary embodiment of the invention, thecalculated values of the concentrations of human blood proteins are mvalues of k different proteins, and the method further comprises thestep of choosing n values out of the m values. Therein k, m and n areintegers and n and m relate to each other as follows: n<m. Furthermoreonly the n values are taken into account for the translation into therisk value.

In other words the exemplary embodiment presented above mayautomatically choose only a subset of the calculated and predictedconcentrations of the human blood proteins for the further processinginto the risk value. Thus, a fast and accurate way of calculating a riskvalue is presented.

Certain criteria may be given in order to define thresholds, definingwhether a protein value is chosen or not. Such thresholds may be storedin e.g. a database and the presented method may compare the calculatedconcentrations of the human blood proteins with the values retrievedfrom that database.

Furthermore, more than one concentration value of each different proteinmay be calculated. In this case the following would be true: m>k.

Additionally, a time development of each concentration of each proteinmay be calculated and predicted. Depending on said development n valuesout of the m values may then be chosen.

However, in each of the above identified cases for m, n and k of thepresent exemplary embodiment, only the n values are taken into a countfor predicting and calculating the risk value.

According to another exemplary embodiment of the invention, the methodcomprises the step graphically displaying a time development of the riskvalue on a graphical user interface.

As can be gathered for example from FIG. 8, this embodiment provides forthe advantage for a user to be able to gather information in anillustrative way about the future development of the risk value. Hence,a fast and reliable decision can be made by the user observing thegraphical representation of the time development of the risk value.Thus, according to another exemplary embodiment of the invention, themethod may comprise the step of calculating a time development of theblood dilution a risk value during or besides the translation. Ifdesired, such a time development of a risk value may be displayed in ax- and y-diagram, wherein the x-axes depicts the time and the y-axesdepicts the blood dilution risk value of clotting and/or embolism and/orbleeding of the blood circulation. In other words the risk value isshown as a function of time.

According to another exemplary embodiment, the at least some of thevalues of the concentrations of the human blood proteins are translatedinto a risk value by means of a numerical function of state variables ofthe numerical model.

Furthermore, said state variables may be used to describe or define adanger zone or a danger level regarding the blood dilution. Such statevariables may be embodied for example as a concentration of proteinsthat play a role in the coagulation process. Such a danger level orpatient stability score may be implemented as an aggregate variablewhich is the numerical function. The numerical model may be used toidentify the set of most sensitive state variables in the situation ofblood dilution, i.e. those protein concentrations that at a smallincrease or decrease of their value, determine whether a coagulationresponse is too strong, i.e. a risk of embolism, or too weak, i.e. riskof excessive bleeding. Different state variables may be related to thetwo different risks, and some strong influences on risk may be due to acombination of multiple parameters. Furthermore, model analysis methodscan be used for such a sensitivity analysis and can be used to identifya panel of state variables. Furthermore, a more exact definition thenumerical function can be estimated through a clinical study where thelevels of the related protein concentrations are measured in case ofbleeding and/or embolism. In other words the use of the numerical modelin the presented embodiment is two-fold. Firstly for the identificationof the panel of state variables, and secondly to predict the expecteddevelopment of the dynamic values of these state variables and thus therisk value during a situation of blood dilution.

According to another exemplary embodiment, the state variables of thenumerical model are chosen from the group comprising concentrations ofthe following proteins: Alpha-2-Macroglobulin (A2M), C4BP, coagulationfactor 10 (F10), F11, F13, prothrombin (F2), tissue factor, F5, F7, F8,F9, fibrinogen, fibrin, protein C, protein S, protein Z, protein Zrelated protein inhibitor (ZPI), alpha-1-anti-trypsin (AAT), protein Cinhibitor (PCI), anti-thrombin (ATIII), PAI1, C1 inhibitor (C1inh),TAFI, TFPI, Vitronectin, plasmin, plasminogen, A2AP, thrombomodulin,uPA, tPA, the proteins' activated forms F10a, F11a, F13a, thrombin(F2a), F5a, F7a, F8a, F9a, activated protein C, and TAFIa, or whereinthe state variables of the numerical model is a concentration ofcomplexes formed by at least two of the previously cited proteins (e.g.FVa-FXa), or wherein the state variables of the numerical model is amass-length ratio of fibrin fibers formed in coagulation. Furthermoreeach protein comprises by one of the following Tables 1 to 3 may bestate variable according to this embodiment.

According to another exemplary embodiment the method further comprisesthe step of using the numerical model to identify a set of mostsensitive state variables in a situation of blood dilution based on atleast one given sensitivity threshold.

The presented embodiment may make use of a comparison between calculatedvalues of the state variables with the sensitivity threshold.

Sensitive state variables can be identified through a sensitivityanalysis method, which involves the variation of one or a set of modelparameters or model inputs and the analysis of the change in one or moremodel outputs as a result of the change in the model parameters or modelinput. The case of sensitive state variable selection in a hemostaticmodel involves the simulation of a clotting response to a certaintrigger, e.g. exposure of proteins residing in blood to a wound in theblood vessel wall, and more specifically to the tissue factor protein atthe wound surface. The varied model inputs are the initial values forthe protein concentrations state variable used in the model, whereas theobserved output can be any model feature that links to the strength ofthe clotting response, e.g. the time between first exposure of the bloodto tissue factor and the moment that thrombin a key protein that isproduced in the clotting process exceeds a threshold value of e.g. 10nM.

In the simplest form of sensitivity analysis one model input is variedwithin its theoretical limits while the other model inputs are keptconstant, i.e. local sensitivity analysis. The resulting change in modeloutput may be translated to a sensitivity score, i.e. single value, forthe one varied model input, where this score will be low if the modeloutput changes little with the varying model input and high when theoutput changes strongly as a result of the varying model input. Such ascore may also depend on the correlation between the change in the modelinput and the change in the model output, i.e. a model output that risesconsistently with a rising model input may lead to a higher sensitivityscore than a model output that changes strongly, but erratically with arising model input. Local sensitivity analysis calculates onesensitivity score for each model input relating to a state variable; thehighest sensitivity scores identify the most sensitive state variables.

In the more complicated global sensitivity analysis all model inputse.g. initial values for the state variables are changed simultaneously.This method is less sensitive to the choice of fixed model inputs (inthe local sensitivity analysis all model inputs but one are fixed to achosen value), but has the downside that the response of the chosenmodel output to the change in a chosen model input is influenced by thevariation of all the other model inputs. This makes the variation of themodel output as a function of the variation of one model input morechaotic by definition. Sensitivity scores can still be calculated, e.g.as the correlation coefficient between a model input and the selectedmodel output (see also Frey, H. C., Patil, S. R., Identification andReview of Sensitivity Analysis Methods. Risk Anal., 2002. 22(3): p.553-578.)

According to another exemplary embodiment of the invention, the methodfurther comprises the step of providing data about human blood proteinlevels of a second blood circulation at a second point in time asreference protein levels. Therein, at the second point in time thesecond blood circulation undergoes bleeding and/or clotting and/orembolism, wherein the second point in time is before the first point intime. Furthermore, the embodiment comprises using said providedreference protein levels for the translation into the risk value for thefirst blood circulation.

Thus, the first blood circulation may be different from the second bloodcirculation, as the reference protein levels may usually be used fromdifferent patients. In other words, this embodiment describes thesituation of the usage of previously gathered information, how bloodcirculations and coagulations systems may react in average during blooddilution. Said provided data may be retrieved by a clinical decisionsupport system from internal or external data storage, wherecorresponding average protein levels during blood dilution are stored.By means of comparing values of the same protein, an estimation of thenumerical function is provided.

This embodiment may also be used to provide for a patient-specificobservation during surgery. In this case the first blood circulation andthe second blood circulation are the same. Thus, if desired, asreference protein levels data may be used of the first bloodcirculation, i.e. data from the same patient, from a previous point intime, at which the patient was undergoing bleeding and/or clottingand/or embolism or which may be related to a bleeding and/or clottingand/or embolism event at a later point in time. Consequently, thepresented method makes use of knowledge, how the protein level of thespecific observed patient is developing under said situations.

According to another exemplary embodiment of the invention, the methodfurther comprises the step of comparing said provided reference proteinlevels with the calculated values of concentration of human bloodproteins, generating a comparison value and using the comparison valuefor the translation into the risk value for the first blood circulation.

According to another exemplary embodiment of the invention, the at leastsome of the values of the concentration of the human blood proteins aretranslated into the risk value by calculating a speed of sealing of ahypothetical wound or by calculating a speed of growth of a hypotheticalthrombus as an output.

In other words, the presented embodiment involves a prediction ofcertain clot elements, like for example fibrin or fibrinogenconcentration. Furthermore, the increase of fiber which comprises orconsists of fibrin or fibrinogen may be calculated. Based on thepreviously cited steps, the presented method may translate this into awound closing time that is necessary to close the hypothetical wound.The presented method may further translate the wound closing time into acorresponding bleeding risk value.

In a similar way based on the calculated concentration of proteins, asize of a thrombus may be calculated. This may further be related to anestimated wound closing. In other words, the numerical model maydescribe how fast a thrombus may grow and thus how fast the sealing of awound takes place, all based on the provided coagulation data. Bothaspects, which are predicted and calculated by the numerical model, i.e.the wound closing time and the growth of a thrombus, are subsequentlytranslated into a corresponding risk value. Thus, the risk value mayfirstly be embodied as a wound closing time risk value or may secondlybe embodied as a thrombus growing risk value. If desired, the calculatedand translated risk value may be based on both predicted results, thewound closing time and the growth of a thrombus.

According to another exemplary embodiment, the numerical model is amodel of a time development of a hypothetical sealing of a wound of thefirst blood circulation. Furthermore, this embodiment further comprisesthe step of performing the simulation of the time development of thehypothetical sealing of the wound of the first blood circulation interms of at least one element of the group comprising a wound surfacearea, interaction of tissue factor in a wound surface area withcoagulation proteins in the first blood circulation, formation of fibrinfibers, and aggregation of blood platelets and/or fibrin fibers, whichcover a wound surface and stop a clotting process. In other words, thevariables that are simulated are of the group comprising the beforecited variables.

Thus the numerical model takes into account at least one of the abovecited elements in order to calculate as an output a wound closing time.This wound closing time may then be translated, as described above, intoa risk value, which may then be graphically displayed to a user.

According to another exemplary embodiment, the method further comprisesthe step of evaluating the simulated time development of thehypothetical sealing of the wound by evaluating a time that passesbetween an initialization of the clotting process, i.e. a formation ofthe wound, and a cessation of the clotting process, i.e. a sealing ofthe wound. Then a risk value may be calculated, namely a bleeding riskvalue.

According to another exemplary embodiment, the method further comprisesthe step of evaluating a risk value based on size and constitution ofthe thrombus. This may be risk value regarding clotting or embolism.

According to another exemplary embodiment of the invention, a clinicaldecision support system for predicting and displaying a blood dilutionrisk value of a first blood circulation is presented wherein the systemcomprises a first arrangement configured to receive measured coagulationdata describing a hemostatic situation of the first blood circulation ata first point in time. The system further comprises a storingarrangement on which a numerical model is stored, wherein the numericalmodel is a mathematical and dynamical representation of a blood dilutionof the first blood circulation. The system further comprises acalculation arrangement configured to perform a simulation of a timedevelopment of the hemostatic situation by means of the numerical modeland based on the measured coagulation data used as an input for thenumerical model. Therein, the calculation arrangement is configured tocalculate values of concentration of human blood proteins as an outputof the simulation. Furthermore, the calculation arrangement isconfigured to translate at least some of the calculated values ofconcentrations of the human blood proteins into a risk value, which riskvalue describes of risk of clotting and/or embolism and/or bleeding ofthe first blood circulation. Furthermore, the system further comprises adisplay arrangement configured to display the risk value.

In addition to the previously described configurations of that clinicaldecision support system, this clinical decision support system may alsobe configured to perform the described methods as presented above and inthe following. Such a clinical decision support system is depicted inthe following FIGS. 1 and 7.

The presented clinical decision support system makes use of a numericalmodel which may be based on biochemical knowledge and/or experiments,which uses the provided calculation data. If desired other patientinformation may also be used to predict a blood dilution risk value infuture. Based on calculated future protein concentrations, a risk valueis calculated by the system and graphically displayed to the user inorder to provide him with the necessary information before loss ofhemostatic balance occurs. Furthermore, the system may be configured tocalculate an effect of different countermeasures that may be performedby the user. The clinical decision support system may be linked to ahospital information system and provides for a continuous estimation ofthe patient's stability. If desired, an alarm when the patient threatensto become unstable may be provided to the user. Additionally one or moresuggestions of optimal countermeasures may be applied to the user by thesystem. The suggestions may be ranked based on the predicted chances ofsuccess.

According to another exemplary embodiment of the invention, a programelement for predicting and displaying a blood dilution risk value of afirst blood circulation, which when being executed by a processor isadapted to carry out steps of receiving measured coagulation datadescribing the hemostatic situation of the first blood circulation at afirst point in time, applying the measured coagulation data as an inputfor a numerical model, the numerical model being a mathematical anddynamical representation of a blood dilution of the first bloodcirculation, and performing a simulation of a time development of thehemostatic situation by means of the numerical model, and based on themeasured coagulation data used as an input for the numerical model, andcalculating values of concentrations of human blood proteins as anoutput of the simulation, and translating at least some of thecalculated values of concentrations of human blood proteins into a riskvalue, which risk value describes a risk of clotting and/or embolismand/or bleeding for the first blood circulation.

The computer program element may be part of a computer program, but itcan also be an entire program by itself. For example, the computerprogram element may be used to update an already existing computerprogram to get to the present invention.

According to another exemplary embodiment, a computer readable medium inwhich a program element for predicting and displaying a blood dilutionrisk value of a first blood circulation is stored, which, when beingexecuted by a processor, is adapted to carry out the steps of receivingmeasured coagulation data describing the hemostatic situation of thefirst blood circulation at a first point in time, and applying themeasured coagulation data as an input for a numerical model, thenumerical model being a mathematical and dynamical representation of ablood dilution of the first blood circulation, and performing asimulation of a time development of the hemostatic situation by means ofthe numerical model and based on the measured coagulation data used asan input for the numerical model, and calculating values ofconcentrations of human blood proteins as an output of the simulation,and translating at least some of the calculated values of concentrationsof human blood proteins into a risk value, which risk value describes arisk of clotting and/or embolism and/or bleeding for the first bloodcirculation.

The computer readable medium, as for example shown in FIG. 1, may beseen as a storage medium, such as for example, a USB stick, a CD, a DVD,a data storage device, a hard disk, or any other medium on which aprogram element as described above can be stored.

It may be seen as a gist of the invention to use a numerical model,which is a mathematical and dynamical representation of a situation ofblood dilution of the blood circulation, to predict future proteinconcentrations of said blood circulation based on the receivedcoagulation data. Time developments of said protein concentrations maythus be calculated and predicted by the model. Based on said predictedprotein concentrations an assessment of the participating proteins isperformed by means of which the most relevant proteins are chosen. Thegroup of chosen proteins is used to calculate and predict a blooddilution risk value, and to show said value or a time development ofsaid value to a user. Corresponding recommendations for countermeasuresmay be predicted and displayed to the user, somehow rated with regard tothe respective chances of success. In other words, the invention is ableto provide for a present risk value based on measured coagulation data,and the invention is additionally able to provide for a predicted riskvalue based on simulation.

Furthermore, a person skilled in the art will gather from the above andthe following description that, unless otherwise notified, in additionto any combination belonging to one type of subject-matter, also anycombination between features relating to different subject-matters, inparticular between features of the apparatus type claims and features ofthe method type claims, is considered to be disclosed with thisapplication. Furthermore, all features can be combined providingsynergetic effects that are more than the simple summation of thefeatures.

The present invention will become apparent from and be elucidated withreference to the embodiments described hereinafter.

Exemplary embodiments of the invention will be described in thefollowing drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically shows a clinical decision support system accordingto an exemplary embodiment of the invention.

FIGS. 2 to 6 schematically show flow diagrams of a method according toexemplary embodiments of the invention.

FIG. 7 schematically shows a clinical decision support system accordingto an exemplary embodiment of the invention.

FIG. 8 schematically shows a graphical user interface to be used inaccordance with an exemplary embodiment of the invention.

In principal, identical parts are provided with the same referencesymbols in the figures.

DETAILED DESCRIPTION OF EMBODIMENTS

FIG. 1 shows a clinical decision support system 100 for calculating anddisplaying a prediction of a blood dilution risk value a first bloodcirculation according to an exemplary embodiment of the invention. Thesystem 100 comprises a first arrangement 101 configured to receivemeasured coagulation data 102 which data describe a hemostatic situationof the first blood circulation at a first point in time. The system 100further comprises a storing arrangement 103 on which a numerical model104 is stored. The numerical model is embodied as a mathematical anddynamical representation of a blood dilution situation of the firstblood circulation. The calculation arrangement 105 is configured toperform a simulation of a time development of the hemostatic situationby means of using the numerical model and supplying the measuredcoagulation data as an input to that model. Furthermore, the calculationarrangement 105 is configured to calculate values of concentrations ofhuman blood proteins as an output of the simulation. Furthermore, thecalculation arrangement 105 is configured to translate at least some ofthe calculated values of concentrations of human blood proteins into arisk value which describes a clotting and/or embolism and/or bleeding ofthe first blood circulation. Furthermore, a display arrangement 106 isshown in FIG. 1. By means of the display arrangement the calculated riskvalue can be shown to the user. Additionally, a program element 107 isshown in FIG. 1 which is configured for calculating and displaying aprediction of a hemostatic situation of a first blood circulation, whenbeing executed by the processor 108. As can be gathered from FIG. 1, acomputer readable medium 109 is shown on which such a program element107 is additionally stored. Moreover, a data storage device 115 is shownwhich is linked to the present clinical decision support system. Forexample, the data storage device 115 may be embodied as a contentinformation system or a content delivery system (CDS). The displayarrangement 106 provides for a graphical user interface 118 which has anarea 116 where the predicted risk value, which has been calculated bymeans of the previously described translation, is displayed. If desired,a time development of that risk value is displayed there to the user. Ascan be seen in FIG. 1, the graphical user interface additionallyprovides for a first arrangement 101 which is configured to receivemeasured coagulation data. FIG. 8 depicts an embodiment of a graphicaluser interface that could be used as user interface 118 of FIG. 1. Incombination with FIG. 8, where also a first arrangement 101 configuredto receive measured coagulation data is shown, it becomes clear thatinformation about e.g. already supplied transfusion units or INR or aPTTtest values may be entered and submitted to the clinical decisionsupport system by the user. Additionally, the graphical user interface118 comprises an area 117 at which recommendations for countermeasurescan be displayed to the user, which countermeasures were calculatedpreviously by the clinical decision support system. These recommendedcountermeasures are based on the model predictions as described aboveand in the following.

In other words, said exemplary embodiment may be seen as a clinicaldecision support system performing a computer-supported decision method.By means of the calculated predictions a user may decide whichadministration of drugs or other clinical action may be useful. Theresult of the prediction may be accompanied by a calculated suggestionof a change of administration of anti- or pro-coagulation drugs. Saidcalculation of the prediction may be performed e.g. on a computer or aprocessor of a computer.

A gist of this mathematical model can be seen in the combination of abiochemical model calculating coagulation cascade and fibrinpolymerization. Thus, “enzymatic conversion” in combination with“complex assembly” can be taken into account during the prediction.

The mathematical model used in FIG. 1 will be explained in more detailhereinafter. This model may be implemented in every herein describedembodiment of the invention. It is of utmost importance that the belowpresented model is just one exemplary embodiment of the mathematicalmodel according to the present invention.

It should be noted that the mathematical model can, if desired, comprisepartially or completely the reaction mechanisms that are disclosed inTables 1 to 3. Also the ordinary differential equations disclosed inTables 4 and 5 may be integrated into the mathematical model accordingto the user's desire. In other words, also a combination betweendifferent reaction mechanisms out of Tables 1 to 3 with ordinarydifferential equations out of Tables 4 and 5 are possible. In otherwords the person skilled in the art may take from the presented tables 1to 5 the features he is interested in regarding his special medicalcase. Thus, it is made clear that the model presented herein is just aversion of the model, and that this model can be adapted, extended,reduced or even completely replaced by another mathematical model whichtakes into account biochemical and pharmacodynamical aspects.

Mathematical Definition of the Model

The mathematical model can be considered to consist of three separatemodules: coagulation cascade, fibrin polymerization and pharmacokineticsand pharmacodynamics (PK/PD) of anticoagulant drugs. If desired only thefirst two modules may be used. Whereas the first two modules are basedon the underlying (protein) interactions of the coagulation response andmay be used to simulate in vitro tests like the thrombin generationassay, prothrombin time (PT) and activated partial thromboplastin time(aPTT), the latter is based on compartment modeling which is used tosimulate the (long-term) kinetics and effect of the anticoagulants suchas unfractionated heparin (UFH) and low-molecular weight heparin (LMWH)in the human body.

Biochemical Model

The physiological system of biochemical reactions may be represented asa closed volume element, representing a certain volume of blood plasmain in vitro tests. Hence, there is no transport in or out of this volumeand clearance of proteins is assumed to be not significant on thistime-scale (minutes). This means that there is conservation of mass inthe volume element. Besides that it is assumed that diffusion in themixture does not significantly influence the reaction velocities.

The mathematical model of the coagulation cascade and fibrinpolymerization consists of 216 state variables (concentrations ofproteins and protein complexes) and 100+ reaction rate constants thatare used to parameterize 91 reactions. An overview of the reactions isgiven in Table 1, Table 2 and Table 3. All states, initialconcentrations and kinetic parameters were defined as non-negative realnumbers, IR+0. The initial concentrations of the proteins are inferredfrom values reported in literature or set to the actual measuredconcentrations. The model's kinetic parameters were estimated fromin-house generated experimental data by means of solving the inverseproblem. Nevertheless, the kinetic parameters (but also the initialconcentrations) are subjected to a continuous update process to improvethe accuracy of the found values by means of additional experiments andanalyses.

The functional description of the state equations can be represented asfollows (in state space formulation).

$\begin{matrix}{{\frac{x}{t} = {f( {{x(t)},{u(t)},\theta} )}},{{{with}\mspace{14mu} x_{0}} = {x( t_{0} )}}} & (1)\end{matrix}$

Where x is the state vector, u the input vector of the test conditions(e.g. certain tissue factor concentration to simulate the PT), x0 is thevector of initial concentrations and f is a vector field with non-linearfunctions parameterized with θ. The output, y, of the state model can becharacterized by:

y(t,θ)= C (t,θ)  (2)

Where matrix C selects a number of ‘interesting’ states of the modeloutput.

The 91 reaction mechanisms derived from literature were classified aseither one of two types of elementary reaction mechanisms. Thesereaction mechanisms were complex assembly and enzymatic conversion.

Complex assembly is the process where substrate A and B react to formcomplex A-B. It features in the formation of coagulation complexes (e.g.FXa-FVa, FIXa-FVIIIa) and inhibition of activated proteins bystochiometric inhibitors (e.g. FIIa-AT-III, TF-FVIIa-FXa-TFPI). Therelated reaction equation reads:

The association rate constant of complex formation, k1, is a secondorder rate constant and the dissociation rate constant of A and B fromA−B, k−1, is a first-order rate constant. In some cases the associationreaction is irreversible, which means the complex is stable and will notdissociate, e.g. inhibition of FIIa by AT-III. Reaction scheme (3) wasconverted to the following set of ordinary differential equations (ODEs)describing the change in concentration, represented by [ . . . ], intime:

$\begin{matrix}{\frac{\partial\lbrack A\rbrack}{\partial t} = {\frac{\partial\lbrack B\rbrack}{\partial t} = {{- \frac{\partial\lbrack {A - B} \rbrack}{\partial t}} = {{- {{k_{1}\lbrack A\rbrack}\lbrack B\rbrack}} + {k_{- 1}\lbrack {A - B} \rbrack}}}}} & (4)\end{matrix}$

The enzymatic conversion of proteins by enzymes was the second type ofreaction mechanism exploited in the coagulation model. All activationprocesses in the hemostasis model correspond to this type of reaction.The reaction scheme of enzymatic conversion can be representedschematically as:

Where E is the enzyme and S the substrate concentration that isconverted into product P by E. Enzymatic conversion of proteins wasimplemented in the mathematical model as follows:

$\begin{matrix}{\frac{\partial\lbrack E\rbrack}{\partial t} = {{- {{k_{1}\lbrack E\rbrack}\lbrack S\rbrack}} + {k_{- 1}\lbrack {E - S} \rbrack} + {k_{2}\lbrack {E - S} \rbrack}}} & (6) \\{\frac{\partial\lbrack S\rbrack}{\partial t} = {{- {{k_{1}\lbrack E\rbrack}\lbrack S\rbrack}} + {k_{- 1}\lbrack {E - S} \rbrack}}} & (7) \\{\frac{\partial\lbrack {E - S} \rbrack}{\partial t} = {{{k_{1}\lbrack E\rbrack}\lbrack S\rbrack} - {k_{- 1}\lbrack {E - S} \rbrack} - {k_{2}\lbrack {E - S} \rbrack}}} & (8) \\{\frac{\partial\lbrack P\rbrack}{\partial t} = {k_{2}\lbrack {E - S} \rbrack}} & (9)\end{matrix}$

Most of the proteins or protein-complexes participate in multiplereactions in the biochemical model, hence all reactions that the proteinor protein-complex is participating in have to be accounted for in theODE of that specific protein's or protein-complex' concentration. Thisresults in one ODE per protein or protein-complex, which consists of asummation of ODE contributions from all reactions that the protein isparticipating in. This is represented mathematically as follows (analternative representation of equation (1)).

$\begin{matrix}{\frac{\partial x}{\partial t} = {{\underset{\_}{S}{\underset{\_}{R}(x)}} = {\sum\limits_{i = 1}^{m}\; {S^{i}{R_{i}(x)}}}}} & (10)\end{matrix}$

Where x is the vector of concentrations of the different substrates, Sis the matrix with reaction rate constants and R is the reaction matrix.Each column of the stochiometric matrix Si corresponds to a particularreaction.

PK/PD Model

The mathematical model that simulates the long-term kinetics and effectsof the anticoagulants is based on a combination of compartment modelsthat are generally used in PK/PD modeling. Since the PK/PD equations arenot as standardized as the biochemical equations (only complex assemblyand enzymatic catalysis), the complete ODEs of each state are shown inTable 4 and Table 5. The ODEs belonging to the pharmacokineticproperties of unfractionated heparin and low-molecular weight heparinare shown in Table 4. As a result of these ODEs the blood kinetics ofboth types of heparin can be calculated. The effects of both heparinsare on the activity of AT-III, and this is represented by equationsv78-v91 in the biochemical model, which uses the blood concentrations ofUFH and LMWH at the moment of blood withdrawal as input. The bloodconcentrations of the coagulation proteins at the moment of bloodwithdrawal are used as input for the biochemical model.

The Tables 1 to 4 are shown in the following. The mathematical modeldescribed herein may thus take into account several or all reactionmechanisms v1 to v91. The person skilled in the art will combine them asneeded or desired. Additionally the ordinary differential equationsdescribed under PKPD1 to PKPD17 may partially or completely beimplemented in the mathematical model.

The used model may also be described as follows: The computer model maybe seen as a representation of the coagulation cascade and fibrinpolymerization as a set of reaction mechanisms. The time dynamics ofeach reaction mechanism may be described as an ordinary differentialequation or ODE that involves the concentration(s) of the protein(s)and/or chemical molecule(s) that are involved in the reaction and thereaction rate parameter(s). By summation of all reaction mechanisms inwhich a particular protein or other kind of chemical molecular isinvolved (a protein or molecule can participate in more than onereaction), the time dynamics of the concentration of that particularprotein or other kind of chemical entity may be calculated. Doing thisfor all proteins or molecules, the whole system can calculate and keeptrack of the evolution of all proteins and molecules over time, howeverfor this one may require, beside the reaction topology, also thenumerical values of the model parameters. These model parameters includethe initial conditions of the system, i.e. the concentration of allproteins and molecules at t=0 (e.g. before onset of bridging therapy),and the reaction rate parameters of the reaction mechanisms. Part of theinitial concentrations that are the most important to the outcome of thesystem are measured from the patient (in the laboratory or clinic),whereas others, less determining proteins, are taken from literature(average patient values, possibly corrected for gender and age, etc.).The reaction rate parameters may be derived via solving an inverseproblem, i.e. model fitting to experimental data. The system of coupledODEs may be solved numerically, using the numerical values of the modelparameters, by employing standard ODE integration algorithms.

TABLE 1 All reaction mechanisms incorporated in the computer model ofthe coagulation cascade. It should be noted that in this table theofficial gene symbole are used instead of the popular scientific namesReaction Name Type Substrates Products Cofactors/Catalyst Reaction sitev1 F3-F7a complex Complex F3, F7a F3-F7a Endothelial membrane assemblyassembly v2 F3-F7 complex Complex F3, F7 F3-F7 Endothelial membraneassembly assembly v3 F7 activation (1) Catalysis F7 F7a F3-F7aEndothelial membrane v4 F7 activation (2) Catalysis F7 F7a F10aEndothelial membrane v5 F7 activation (3) Catalysis F7 F7a F9aEndothelial membrane v6 F7 activation (4) Catalysis F7 F7a F2a v7 F9activation (1) Catalysis F9 F9a F11a, negative phospholipids v8 F9activation (2) Catalysis F9 F9a F3-F7a Endothelial membrane v9 F9adegradation Degradation F9a Blood plasma v10 F8 activation (1) CatalysisF8 F8a F2a Blood plasma?? v11 F8 degradation Degradation F8aPROCa-PROS1-F5ac Platelet membrane v12 F9a-F8a complex Complex F9a, F8aF9a-F8a Ca2+, neg phospholipid Platelet membrane assembly assembly v13F2 activation (1) Catalysis F2 F2a F10a Blood plasma v14 F2 activation(2) Catalysis F2 F2a F10a-F5a Platelet membrane v15 F2a degradationDegradation F2a Blood plasma v16 F5 activation Catalysis F5 F5a F2aBlood plasma v17 F5 anticoagulant Catalysis F5 F5ac PROCa Blood plasmaformation v18 F5a degradation Degradation F5a PROCa-PROS1 Blood plasma/endothelial membrane v19 F10 activation (1) Catalysis F10 F10a F3-F7aEndothelial membrane v20 F10 actication (2) Catalysis F10 F10a F9a-F8aPlatelet membrane v21 F10 activation (3) Catalysis F10 F10a F9a Bloodplasma?? v22 F10a degradation Degradation F10a Blood plasma v23 F10a-F5acomplex Complex assembly F10a, F5a F10a-F5a Ca2+, neg phospholipidPlatelet membrane assembly v24 PROC activation (1) Catalysis PROC PROCaF2a Blood plasma v25 PROS1-C4BP Complex assembly PROS1, C4BP PROS1-C4BPBlood plasma complex assembly v26 PROCa-PROS1 Complex assembly PROCa,PROS1 PROCa-PROS1 Ca2+, neg phospholipid Platelet membrane complexassembly v27 PROCa-PROS1- Complex PROCa-PROS1, PROCa-PROS1- Ca2+, negphospholipid Platelet membrane F5ac assembly F5ac F5ac complex assemblyv28 F13 activation Catalysis F13 F13a F2a, Ca2+ (at least Blood plasma 1mM) v29 F12 activation (1) Catalysis F12 F12a F12a, negative Negativesurface phospholipds v30 F12 activation (2) Catalysis F12 F12a KLKB1aBlood plasma v31 F12 activation (3) Catalysis F12 F12a KNG1 Blood plasmav32 F12a degradation Degradation F12a Blood plasma?? v33 KLKB1activation Catalysis KLKB1 KLKB1a F12a Blood plasma v34 F11 activation(1) Catalysis F11 F11a F12a Blood plasma v35 F11 activation (2)Catalysis F11 F11a F2a, negative Negative surface phospholipids v36 F11activation (3) Catalysis F11 F11a F11a, negative Negative surfacephospholipids v37 F11a degradation Degradation F11a Blood plasma v38CPB2 activation (1) Catalysis CPB2 CPB2a F2a Blood plasma v39 CPB2adegradation Degradation CPB2a Blood plasma v40 F10a-TFPI complex Complexassembly TFPI, F10a F10a-TFPI Blood plasma?? assembly v41F10a-F3-F7a-TFPI Complex assembly F10a-TFPI, F3-F7a F10a-F3-F7a-TFPICa2+ Endothelial membrane complex assembly v42 F3-F7a-TFPI Complexassembly F3-F7a, TFPI F3-F7a-TFPI Endothelial membrane complex assemblyv43 F11a-SERPINC1 Complex assembly F11a, SERPINC1 F11a-SERPINC1 SERPIND1Blood plasma complex assembly v44 F12a-SERPINC1 Complex assembly F12a,SERPINC1 F12a SERPINC1 SERPIND1 Blood plasma complex assembly v45F9a-SERPINC1 Complex assembly F9a, SERPINC1 F9a-SERPINC1 SERPIND1 Bloodplasma complex assembly v46 F2a-SERPINC1 Complex assembly F2a, SERPINC1F2a-SERPINC1 SERPIND1 Blood plasma complex assembly v47 F10a-SERPINC1Complex assembly F10a, SERPINC1 F10a-SERPINC1 SERPIND1 Blood plasmacomplex assembly v48 F3-F7a-SERPINC1 Complex assembly F3-F7a, SERPINC1F3-F7a-SERPINC1 SERPIND1 Blood plasma complex assembly v49PROCa-SERPINA1 Complex assembly PROCa, SERPINA1 PROCa-SERPINA1 Bloodplasma complex assembly v50 PROCa-SERPINA5 Complex assembly PROCa,SERPINA5 PROCa-SERPINA5 Heparin dependent Blood plasma complex assemblyv51 F2a-SERPINA5 Complex assembly F2a, SERPINA5 F2a-SERPINA5 Heparindependent Blood plasma complex assembly v52 F10a-SERPINA5 Complexassembly F10a, SERPINA5 F10a-SERPINA5 Heparin dependent Blood plasmacomplex assembly v53 KLK1a-SERPINA5 Complex assembly KLKB1a,KLKB1a-SERPINA5 Blood plasma?? complex assembly SERPINA5 v54PROZ-SERPINA10 Complex assembly PROZ, SERPINA10 PROZ-SERPINA10 Bloodplasma complex assembly v55 F9a-SERPINA10 Complex assembly F9a,SERPINA10 F9a-SERPINA10 Blood plasma complex assembly v56 F10a-PROZ-Complex assembly PROZ-SERPINA10, F10a-PROZ- Ca2+, Phospholipids MembraneSERPINA10 F10a SERPINA10 complex assembly v57 F11a-SERPINA10 Complexassembly F11a, SERPINA10 F11a-SERPINA10 Blood plasma complex assemblyv58 PROCa-SERPINE1 Complex assembly PROCa, SERPINE1 PROCa-SERPINE1 Bloodplasma?? complex assembly v59 F2a-SERPINE1 Complex assembly F2a,SERPINE1 F2a-SERPINE1 Blood plasma complex assembly v60 VTN-SERPINE1Complex assembly VTN, SERPINE1 VTN-SERPINE1 Membrane surface complexassembly v61 F2a-VTN- Complex assembly F2a, VTN- F2a-VTN-SERPINE1Membrane surface SERPINE1 SERPINE1 complex assembly v62 CPB2a-SERPINE1Complex assembly CPB2a, SERPINE1 CPB2a-SERPINE1 Blood plasma complexassembly v63 SERPINE1 Degradation SERPINE1 Blood plasma?? degradationv64 F11a-SERPINE1 Complex assembly F11a, SERPING1 F11a-SERPING1 Bloodplasma?? complex assembly v65 F12a-SERPING1 Complex assembly F12a,SERPING1 F12a-SERPING1 Blood plasma?? complex assembly v66KLKB1-SERPING1 Complex assembly KLKB1a, KLKB1a-SERPING1 Blood plasma??complex assembly SERPING1 v67 F2a-α2-M complex Complex assembly F2a,α2-M F2a-α2-M assembly v68 Substrate catalysis Catalysis subs subsa F2av69 Substrate catalysis Catalysis subs subsa F2a-α2-M

TABLE 2 All reaction mechanisms incorporated in the computer model ofthe fibrin polymerization. Reaction Name Type Substrates ProductsCofactors/Catalyst Reaction site v70 FpA cleavage from Fg Catalysis FgdesAA-Fg, 2 FpA F2a Blood plasma v71 FpB cleavage from Fg Catalysis FgdesBB-Fg, 2 FpB F2a Blood plasma v72 FpA cleavage from CatalysisdesAA-Fg Fn, 2 FpA F2a Blood plasma desAA-Fg v73 FpB cleavage fromCatalysis desBB-Fg Fn, 2 FpB F2a Blood plasma desBB-Fg v74 FpA cleavagefrom Catalysis Fg-F2a desAA-Fg-F2a F2a Blood plasma Fg-F2a v75 FpBcleavage from Catalysis Fg-F2a desBB-Fg-F2a F2a Blood plasma Fg-F2a v76Protofibril Complex assembly* P_(n), P_(m) P_(n+m) Blood plasmaformation/growth v77 Fiber Complex assembly** F_(o), F

F_(n+m) F_(n) Blood plasma formation/growth *P_(n) + P

→P

V_(n+m) ≦ 29, n > 0, m > 0 **P_(n) + P_(p)→P

 V_(n+p) ≦ 9.0 > 0, p > 0

indicates data missing or illegible when filed

TABLE 3  

 in the computer model regarding the effect of  

  and low-molecul 

  weight h 

 (LMWH) on the function of  

  should be noted that  

 symbol of  

  is used instead of the popular scientific names. Reaction Name TypeSubstrates Products Cofactors/Catalyst Reaction site v78 SERPINC1-UFHComplex assembly SERPINC1, UFH SERPINC1-UFH Blood plasma complexassembly v79 F11a-SERPINC1-UFH Complex assembly F11a, SERPINC1-UFHF11a-SERPINC1-UFH Blood plasma complex assembly v80 F9a-SERPINC1-UFHComplex assembly F9a, SERPINC1-UFH F9a-SERPINC1-UFH Blood plasma complexassembly v81 F2a-SERPINC1-UFH Complex assembly F2a, SERPINC1-UFHF2a-SERPINC1-UFH Blood plasma complex assembly v82 F10a-SERPINC1-UFHComplex assembly F10a, SERPINC1-UFH F10a-SERPINC1-UFH Blood plasmacomplex assembly v83 F3-F7a-SERPINC1- Complex assembly F3-F7a, SERPINC1-F3-F7a-SERPINC1- Blood plasma UFH complex UFH UFH assembly v84F10a-F5a-SERPINC1- Complex assembly F10a-F5a, SERPINC1-F10a-F5a-SERPINC1- Blood plasma UFH complex UFH UFH assembly v85SERPINC1-LMWH Complex assembly SERPINC1, LMWH SERPINC1-LMWH Blood plasmacomplex assembly v86 F11a-SERPINC1- Complex assembly F11a, SERPINC1-F11a-SERPINC1- Blood plasma LMWH complex LMWH LMWH assembly v87F9a-SERPINC1-LMWH Complex assembly F9a, SERPINC1- F9a-SERPINC1-LMWHBlood plasma complex assembly LMWH v88 F2a-SERPINC1-LMWH Complexassembly F2a, SERPINC1- F2a-SERPINC1-LMWH Blood plasma complex assemblyLMWH v89 F10a-SERPINC1- Complex assembly F10a, SERPINC1- F10a-SERPINC1-Blood plasma LMWH complex LMWH LMWH assembly v90 F3-F7a-SERPINC1-Complex assembly F3-F7a, SERPINC1- F3-F7a-SERPINC1- Blood plasma LMWHcomplex LMWH LMWH assembly v91 F10a-F5a-SERPINC1- Complex assemblyF10a-F5a, SERPINC1- F10a-F5a-SERPINC1- Blood plasma LMWH complex LMWHLMWH assembly

indicates data missing or illegible when filed

TABLE 4 The ordinary differential equations 

Reaction Name Equation Description PKPD1 UFH in blood compartment$\frac{d\lbrack{UFH}\rbrack}{dt} = {\frac{IV}{{Vd}_{UPH}} - {k{\text{?}\;\lbrack{UFH}\rbrack}}}$[UFH]: concentration of UFH in blood compartment PKPD2 LMWH inabsorption compartment$\frac{{dA}_{LMWH}}{dt} = {{- k}\text{?}\mspace{11mu} A_{LMWH}}$A_(LMWH): amount of LMWH in absorption compartment PKPD3 LMWH in bloodcompartment $\begin{matrix}{\frac{d\lbrack{LMWH}\rbrack}{dt} = {{k\text{?}\; \frac{A_{LMWH}}{{Vc}_{LMWH}}} - {k{\text{?}\;\lbrack{LMWH}\rbrack}} +}} \\{{k\text{?}\; \frac{A_{{LMWH},p}}{{Vc}_{LMWH}}} - {k{\text{?}\;\lbrack{LMWH}\rbrack}}}\end{matrix}\quad$ [LMWH]: concentration of LMWH in blood compartmentPKPD4 LMWH in peripheral compartment $\begin{matrix}{\frac{{dA}_{{LMWH},p}}{dt} = {{k{\text{?}\;\lbrack{LMWH}\rbrack}\mspace{11mu} {Vc}_{LMWH}} -}} \\{k\text{?}\mspace{11mu} A_{{LMWH},p}}\end{matrix}\quad$ A_(LMWH): amount of LMWH in peripheral compartment

indicates data missing or illegible when filed

As mentioned, the pharmacodynamical modeling and/or pharmacokineticmodeling may not be used in some embodiments of the invention, if theuser desires to do so. Compared to embodiments which use models thatinvolve pharmacodynamical modeling and/or pharmacokinetic modeling, theembodiments avoiding such usage do not need to calculate certainaspects, as no drug distribution and interaction with the body isnecessary. Therefore, said embodiments are faster in providing the userwith the necessary information as said embodiments including PK/PDmodeling.

Furthermore, the numerical model used in said embodiments which avoidsaid usage of PK/PD modeling may work on a shorter time scale, i.e.minutes rather than days.

When including the countermeasures, specifically relating to theadministration of pro- and anti-coagulants it might be an advantage toprovide for PK/PD modeling. Administrations may be directly into thebloodstream, so the part of PK/PD modeling that represents uptake by thebody, digestion by the liver etc may not be necessary as one can simplymodel it as a direct increase of the concentration of the drug in theblood and time scales are indeed much shorter. Interaction of a drugwith the other blood proteins is however required if the user wants topredict the effect of a countermeasure involving a drug. The presentinvention matches theses needs.

FIG. 2 is a flow diagram of a method of predicting a blood dilution riskvalue of a first blood circulation, wherein the presented methodcomprises the steps S1 to S5. Providing measured coagulation datadescribing the hemostatic situation of a first blood circulation at afirst point in time is depicted as step S1. Furthermore, applying themeasured coagulation data as an input for a numerical model is presentedas step S2, wherein the numerical model is mathematical and dynamicalrepresentation of a blood dilution situation of the first bloodcirculation. Moreover, in step S3, a simulation of a time development ofthe hemostatic situation by means of the numerical model and based onthe measured coagulation data used as an input for the numerical modelis performed. In step S4, a calculation of values of concentrations ofhuman blood proteins as an output of the simulation is performed. Thestep S5 describes the translating of at least some of the calculatedvalues of the concentrations of the human blood proteins into a riskvalue, which risk value describes a risk of clotting and/or embolismand/or bleeding for the first blood circulation.

To perform the presented method a CDS system may be used thatincorporates all of the monitored and additional patient data and usesthis to provide an early warning when the patient's hemostatic levelstarts to move into the danger zone, and provides advice on the optimalcountermeasure to regain hemostatic balance. The solution uses acomputer model that takes into account the patient's data and theestimated dilution of the blood (through blood loss and transfusions)and predicts when the patient's hemostatic balance shifts to a dangerouslevel. The same model can simulate the expected effect of a number ofprocedures that are designed to regain hemostatic balance, and recommendthe procedure with the highest chance of success. The model may make itscalculation near instantaneously, before e.g. a next test is performed.This may provide the anaesthesiologist with the opportunity to keep thepatient within the stable hemostatic range, instead of returning thepatient to safety after (s)he has left the stable range. If the patientshould stray outside the stable range after all, the model can estimatethe expected results for each of a set of available countermeasures.This will allow the anaesthesiologist to choose the optimalcountermeasure, performed in the optimal way, and thus to stabilize thepatient as fast as possible.

The aforementioned model used in the embodiment of FIG. 2 may beimplemented as a differential equation model that describes inter aliathe interactions of coagulation proteins and formation and break-down ofthe thrombus. Such model is a dynamic model, as it describes theevolution of system states like protein concentrations or thrombus sizeover time. The time dynamics of each interaction mechanism is describedas an ordinary differential equation or ODE that involves theconcentration(s) of the protein(s) and/or chemical molecule(s) that areinvolved in the reaction and the reaction rate parameter(s). Bysummation of all reaction mechanisms in which a particular protein orother kind of chemical molecular is involved (a protein or molecule canparticipate in more than one reaction), the time dynamics of theconcentration of that particular protein or other kind of chemicalentity is calculated. The whole system can be calculated by keepingtrack of the evolution of all proteins and molecules.

As model parameters, the initial conditions of the system, i.e. theconcentration of all proteins and molecules at t=0 and the reaction rateparameters of the reaction mechanisms may be entered into the model by auser. Part of the initial concentrations may also be measured in alaboratory or a clinic, whereas others may be taken from literature,i.e. average patient values, possibly corrected for gender and age. Thereaction rate parameters may be derived via solving an inverse problem,i.e. model fitting to experimental data. The system of ODEs may besolved numerically, using the numerical values of the model parameters,by employing ODE integration algorithms.

The method of FIG. 2 may comprise the step of automatically suggestingan application for administration of a coagulant and/or ananti-coagulant.

FIG. 3 shows another exemplary embodiment of a method of predicting ahemostatic situation of a first blood circulation by means of a flowdiagram. With regard to steps S1 to S4 and S5, it is referred to thepresented disclosure and description of FIG. 2. However, compared toFIG. 2, the embodiment of FIG. 3 provides a step S6. The method of FIG.3 specifies that the calculated values of the concentrations of thehuman blood proteins are m values of k different proteins. The step S6defines to choose n values out of the m values. Therein, k, m and n areintegers and n<m. Furthermore, only the n values are taken into accountfor the translation into the risk value which is performed in step S5.Moreover, the method of FIG. 3 defines to graphically display a timedevelopment of the risk value on a graphical user interface within stepS7. If desired, additional information may be stored on for example thestoring arrangement 103 of FIG. 1, which information is used by thepresented method in order to decide which n values out of the m valuesare chosen. For example, only the most sensitive types of human bloodproteins are chosen out of the proteins for which concentrations havebeen calculated. However, also other criteria may be applied.

FIG. 4 describes another exemplary embodiment of a method of predictinga hemostatic situation of a first blood circulation according to anembodiment of the present invention. Regarding steps S1 to S5, it iskindly referred to the description and disclosure of FIG. 2.Additionally, step S8 is comprised by the method of FIG. 4. The step ofusing the numerical model to identify a set of most sensitive statevariables in a situation of blood dilution based on at least one givensensitivity threshold is depicted by step S8. Details additional aboutsuch an identification have been described already above and saiddetails may be integrated in the embodiment of FIG. 4.

FIG. 5 shows another exemplary embodiment of a method according to thepresent invention. Providing measured coagulation data which describethe hemostatic situation of the first blood circulation at a first pointin time is depicted with step S1. Additionally, data about human bloodprotein levels of a second blood circulation at a second point in timeas reference protein levels are provided, which provision is depicted bystep S9. The first and the second blood circulation may be the same,which would lead to a specific observation of one patient. Furthermore,the second point in time is before the first point in time and the bloodcirculation undergoes bleeding and/or clotting and/or embolism at thesecond point in time. Therefore, it can be assured that the usedreference protein levels indicate realistic levels of the correspondingproteins that occur during a bleeding and/or clotting and/or embolism.However, if desired, the first and the second blood circulation may bedifferent, which means that reference values are retrieved from anotherpatient. Regarding steps S2 to S4, it is kindly referred to thepreviously presented description of FIG. 2. Furthermore, comparing saidprovided reference protein levels with the calculated values ofconcentrations of human blood proteins is depicted in FIG. 5 by stepS11. Generating a comparison value, 512, and using the comparison valuefor the translation into the blood dilution risk value for the firstblood circulation, S13, complete the presented method. There is to benoted that step S5 is performed in this embodiment in such a way thatthe risk value is calculated based on the comparison value waspreviously generated.

FIG. 6 shows another flow diagram of a method according to an exemplaryembodiment of the invention. Regarding steps S1 to S4, it is kindlyreferred to the disclosure of previously described FIG. 2. As can beseen in FIG. 6, three different possibilities of how to proceed duringor after step S4 are presented by FIG. 6. In detail, FIG. 6 disclosesthree different embodiments of the present invention. The flow diagramof FIG. 6 describes that at least some of the predicted values of theconcentrations of the human blood proteins are translated into the riskvalue by either calculating a speed of sealing of a hypothetical wound,step S14 a, or by calculating an extent of growth of a hypotheticalthrombus (i.e. the resulting thrombus size), step S14 b, as an output.FIG. 6 depicts, that firstly only the aspect of the speed of sealing ofthe hypothetical wound can be calculated. In such a case, the user wouldchoose to use the left branch of FIG. 6. In case he is interested incalculating a size of the hypothetical thrombus, step S14 b, he wouldchoose the centered branch of FIG. 6. However, if the user desires to beprovided with results of both calculations, he may choose the rightbranch of FIG. 6. In any of the three presented branches, based on thepreviously calculations and based on the generated correspondingoutputs, a translation into the risk value is performed, which riskvalue describes a risk of clotting and/or embolism and/or bleeding forthe first blood circulation. This is described by the corresponding stepS5.

In other words, the embodiment of FIG. 6 comprises a model which is usedto simulate the growth of the thrombus or sealing of a hypotheticalwound itself. If the model predicts uncontrolled growth of the thrombus,to the point where it occludes a vessel or may break into circulatingpieces, and if no countermeasure is taken, the patient stability scoreor risk value will exceed one threshold of the save zone. In case themodel of FIG. 6 predicts a sealing of the wound which is so slow thatblood loss grows to a dangerous level, the save zone has been left onthe other side. However, the present invention provides for improvementsof such situations, as an alert may be applied to the user.Alternatively counter measures may be suggested or at least a predictionof how the risk value will evolve is supplied to the user.

If desired, any of the three embodiments of FIG. 6 may comprise theadditional step of evaluating the simulated time development of thehypothetical sealing of the wound by evaluating a time that passesbetween an initialization of the clotting process, i.e. a formation ofthe wound, and a cessation of the clotting process, i.e. a sealing ofthe wound.

If desired, any of the embodiments described within FIG. 6, mayadditionally comprise a step of evaluating the risk of occlusion of ablood vessel by evaluating a size of the thrombus. This may be expressede.g. as the total mass of fibrin present in the thrombus, the volume ofthe thrombus or the thickness of the thrombus, i.e. the minimum ormaximum distance between the wound and/or blood vessel wall and theouter edge of the thrombus.

Additionally if desired, any of the three embodiments of FIG. 6 maycomprise the additional step of evaluating the risk of embolism byevaluating a constitution of the thrombus. This may for example beembodied through the calculation of the thrombus' mass density or themass-length ratio of the comprising fibrin fibers for the purpose ofrisk of embolism, caused by pieces breaking off of the main thrombus.

FIG. 7 shows a clinical decision support system according to anexemplary embodiment of the invention. In the following, clinicaldecision support system will be described by the shown basic structureof FIG. 7; however, it will become apparent that such a clinicaldecision support system may comprise many additional optional features.In general the clinical decision support system of FIG. 7 provides asolution to the above identified problems of the prior art and maycomprise of a content delivery system that may incorporate all of themeasured and monitored and, if desired, additional patient data and mayuse this to provide an early warning when the patient's hemostatic levelstarts to move into the danger zone. Furthermore the clinical decisionsupport system of FIG. 7 may provide for an advice on the optimalcountermeasure to a user such hemostatic balance is regained. Theclinical decision support system makes use of or comprises a computermodel that takes into account the patient's data and the estimateddilution of the blood through e.g. blood loss and e.g. transfusions.Furthermore the computer model may be configured to predict when thepatient's hemostatic balance shifts to a dangerous level. The same modelmay be used to simulate the expected effect of a number of proceduresi.e. countermeasures that are designed to regain hemostatic balance, andmay further recommend the procedure i.e. countermeasure with the highestchance of success to the user.

One advantage of the use of the computer model according to theexemplary embodiment of FIG. 7 may be seen in that it can calculate e.g.the effect of the administration of the next unit of transfusion fluidand subsequent dilution of the blood in terms of existing diagnosticvalues like INR or thrombo-elastometry measurement outputs. The modelcan make this calculation near instantaneously, before the unit isactually administered, and before the next test is performed. Thisprovides the anesthesiologist with the opportunity to keep the patientwithin the stable hemostatic range, instead of returning the patient tosafety after the patient has left the stable range. If the patientshould stray outside the stable range after all, the model can estimatethe expected results for each of a set of available countermeasures.This will allow the anaesthesiologist to choose the optimalcountermeasure, performed in the optimal way, and thus to stabilize thepatient as fast as possible.

Regarding the computer model of this exemplary embodiment of FIG. 7 thefollowing should be noted. The aforementioned model may be implementedas a differential equation model that describes e.g. the interactions ofcoagulation proteins, formation and break-down of the thrombus, theeffect of anti-coagulants like Heparin, etc. Said model may be seen as adynamic model which describes the evolution of states variables of themodel like e.g. protein concentrations or e.g. thrombus size over time.The time dynamics of each interaction mechanism is described as anordinary differential equation or ODE that may involve theconcentration(s) of the protein(s) and/or chemical molecule(s) that areinvolved in the reaction and the reaction rate parameter(s). Bysummation of some or all reaction mechanisms in which a particularprotein or other kind of chemical molecular is involved, a protein ormolecule can participate in more than one reaction, the time dynamics ofthe concentration of that particular protein or other kind of chemicalentity is calculated. The whole system can be calculated by keepingtrack of the evolution of some or all proteins and molecules. Thus thepresented dynamic model can predict future evolution of the patient'shemostatic system based on e.g. measurements of the present.

In certain cases this may require however that besides the reactiontopology, the numerical values of the model parameters are known aswell. These model parameters include the initial conditions of thesystem, i.e. the concentration of all proteins and molecules at t=0,e.g. before any blood loss, and the reaction rate parameters of thereaction mechanisms. Part of the initial concentrations may be measuredin the laboratory or clinic, whereas others may be taken fromliterature, i.e. average patient values, that are possibly corrected forgender and age. The reaction rate parameters may be derived via solvingan inverse problem, i.e. model fitting to experimental data. The systemof ODEs may be solved numerically, using the numerical values of themodel parameters, by employing ODE integration algorithms. The treatingphysician is the operator of the clinical decision support system, henceis able to give user input to the system. The other type of input can beclinical measurements, e.g. INR, aPTT, vitamin K-proteins' activity. Theuser interface is connected via software to the computer model; theinformation flow of the user interface is diverted to the computer modelto serve as input. The computer model uses the given input andcalculates the expected future evolution of the blood dilution of theblood circulation, which are forwarded to the user interface via theconnecting software. The clinical decision support system of FIG. 7further comprises a user interface. This exemplary embodiment of theinvention may be integrated in e.g. a hospital IT system, and can beaccessed via a graphical user interface on one of the computerterminals. The clinical decision support system can be provided withcoagulation data about the patient that is available before instabilityin the haemostatic situation due to blood dilution is feared. This maybe done either manually or through a retrieval of data from a datastorage like e.g. a server or e.g. through a direct interface with aninformation system like a hospital information system or an electronicpatient records. This may be seen in FIG. 8, top left panel. Theclinical decision support system may interface with the monitoring andmeasurement devices that are used during a surgery, and/or have a simpleway to enter new patient data into the system manually. This may be seenin FIG. 8, bottom left panel. One screen of the clinical decisionsupport system should show the current risk value of the patient, e.g.visualized by a graph that plots a ‘safety value’ or a set of values forthe patient over time and indicates when this value threatens to leavethe safe zone. This may be seen in FIG. 8, top right panel. Anindication of threat may be delivered to the user both in the graph andthrough an alarm. Such risk values can be as simple as INR or aPTTvalues, or a more elaborate diagnostic score value.

In more detail, FIG. 7 describes a clinical decision support system 700,which comprises a first arrangement 701, which is embodied as a userinterface. Furthermore, it is shown that coagulation data 702 aresupplied by means of an input 707 of a user into the user interface 701.Software 709 is shown in FIG. 7 as well as a storing arrangement 703,which simultaneously acts as a calculation arrangement 705. Thenumerical model 704 is stored on the storing arrangement 703.Additionally, clinical measurements or clinical data 706 may be suppliedto the user interface. In combination with the following description ofFIG. 8, the advantages and gist of the system will become even moreapparent.

FIG. 8 shows a user interface as may be comprised by a clinical decisionsupport system according to an exemplary embodiment of the invention. InFIG. 8 the top right panel shows both the observed risk values, shown inthe left zone 802 and the predicted risk values in the right zone 803for the monitored risk value. The observed risk values can be calculatedby the numerical model based on the provided coagulation data which areprovided via the bottom-left panel. Moreover, the numerical model canpredict a risk value based on calculated predictions of theconcentrations of the participating proteins. Upon prediction of leavingthe safe zone 805 the clinical decision support system may display oneor a number of countermeasures 807 to 810 to the screen, sorted by theirprobability of success and paired to a preference score indicating thesystem's preference for each measure, see bottom right panel of FIG. 8.It should be noted that this example and illustration is meant toexplain the type of graphical user interface that may be used inaccordance with the present invention, and is in no way exhaustive ine.g. the list of possible test values to enter or recommended actions tooutput. Additionally, the time development of risk value 801 is depictedin the graphical user interface 800. The separation between the area 802describing the observed risk values and the area 803 of the predictedrisk values can clearly be gathered from the top right panel of FIG. 8.In other words, the clinical decision support system is configured toprovide for a calculated risk value based on measured coagulation data,and the clinical decision support system is additionally configured toprovide for a predicted risk value based on simulation.

As can be seen in FIG. 8 the graphical representation of risk value 801is displayed in an x and y diagram which provides on the y-axes a stablezone 805 detailed as an area which the risk value may develop withoutcausing an alert. Furthermore, a danger zone 804, i.e. a danger area isshown in FIG. 8. An alert sign 806 is comprised by the graphical userinterface. Furthermore, personal data about the patient can be receivedfrom external or internal databases and shown on the top left panel.Personal information 814 is displayed to the user. By means of accessbutton 813, a connection to a database may be established. Furthermore,measured coagulation data 102 can be received by the graphical userinterface via the bottom left panel. By means of button 812, thisreceived and measured coagulation data may be submitted to theunderlying numerical model in order to perform the desired predictions.Measured coagulation data may be provided to the receiving arrangement101. Exemplarily values of a ROTEM tests are shown, which is a brandname. In general this may be called a value of a thrombo-elastometrytest.

1. Method of predicting a blood dilution risk value of a first bloodcirculation, the method comprising the steps: providing measuredcoagulation data describing a haemostatic situation of the first bloodcirculation at a first point in time (S1), applying the measuredcoagulation data as an input for a numerical model, the numerical modelbeing a mathematical and dynamical representation of a blood dilution ofthe first blood circulation (S2), performing a simulation of a timedevelopment of the haemostatic situation by means of the numerical modeland based on the measured coagulation data used as an input for thenumerical model (S3), calculating values of concentrations of humanblood proteins as an output of the simulation (S4), and translating atleast some of the calculated values of the concentrations of the humanblood proteins into a risk value, which risk value describes a risk ofclotting and/or embolism and/or bleeding for the first blood circulation(S5).
 2. Method according to claim 1, wherein the calculation of thevalues of the concentrations of the human blood proteins is generating apredicted time development of said concentrations of human bloodproteins as the output of the simulation.
 3. Method according to claim1, wherein the calculated values of the concentrations of the humanblood proteins are m values of k different proteins, the method furthercomprising the step: choosing n values out of the m values (S6), whereink, m and n are integers and n<m, and wherein only the n values are takeninto account for the translation into the risk value.
 4. Methodaccording to claim 1, further comprising the step: graphicallydisplaying a calculated time development of the risk value (801) on agraphical user interface (S7).
 5. Method according to claim 1, whereinthe at least some of the values of concentrations of human bloodproteins are translated into the risk value by means of a numericalfunction of state variables of the numerical model.
 6. Method accordingto claim 5 wherein the state variables of the numerical model are chosenfrom the group comprising concentrations of the following proteins:Alpha-2-Macroglobulin (A2M), C4BP, coagulation factor 10 (F10), F11,F13, prothrombin (F2), tissue factor, F5, F7, F8, F9, fibrinogen,fibrin, protein C, protein S, protein Z, protein Z related proteininhibitor (ZPI), alpha-1-anti-trypsin (AAT), protein C inhibitor (PCI),anti-thrombin (ATIII), PAI1, C1 inhibitor (C1inh), TAFI, TFPI,Vitronectin, plasmin, plasminogen, A2AP, thrombomodulin, uPA, tPA, theproteins' activated forms F10a, F11a, F13a, thrombin (F2a), F5a, F7a,F8a, F9a, activated protein C, and TAFIa, or wherein the state variablesof the numerical model comprise a concentration of complexes formed byat least two of the previously cited proteins (e.g FVa-FXa), or whereinthe state variables of the numerical model comprise a mass-length ratioof fibrin fibers formed in coagulation.
 7. Method according to claim 5,further comprising the step: using the numerical model to identify a setof most sensitive state variables in a situation of blood dilution basedon at least one given sensitivity threshold (S8).
 8. Method according toclaim 1, further comprising the step: providing data about human bloodprotein levels of a second blood circulation at a second point in timeas reference protein levels (S9), wherein at the second point in timethe second blood circulation undergoes bleeding and/or clotting and/orembolism, wherein the second point in time is before the first point intime, and using said provided reference protein levels for thetranslation into the risk value for the first blood circulation (S10).9. Method according to claim 1, wherein the at least some of the valuesof the concentrations of the human blood proteins are translated intothe risk value by calculating a speed of sealing of a hypothetical wound(S14 a) as the output or by calculating an extent of growth of ahypothetical thrombus as the output (S14 b).
 10. Method according toclaim 9, wherein the numerical model is a model of a time development ofa hypothetical sealing of a wound of the first blood circulation,further comprising the steps: performing the simulation of the timedevelopment of the hypothetical sealing of the wound of the first bloodcirculation in terms of at least one element of the group comprising: awound surface area, interaction of tissue factors in a wound surfacearea with coagulation proteins in the first blood circulation, formationof fibrin fibers, and aggregation of blood platelets and/or fibrinfibers, which cover a wound surface and stop a clotting process. 11.Method according to claim 10, further comprising the steps: evaluatingthe simulated time development of the hypothetical sealing of the woundby evaluating a time that passes between an initialization of theclotting process, i.e. a formation of the wound, and an cessation of theclotting process, i.e. a sealing of the wound.
 12. (canceled) 13.Clinical decision support system for predicting and displaying a blooddilution risk value of a first blood circulation, the system comprising:a first arrangement configured to receive measured coagulation datadescribing a haemostatic situation of the first blood circulation at afirst point in time, a storing arrangement on which a numerical model isstored, wherein the numerical model is a mathematical and dynamicalrepresentation of a blood dilution of the first blood circulation, acalculation arrangement configured to perform a simulation of a timedevelopment of the haemostatic situation by means of the numerical modeland based on the measured coagulation data used as an input for thenumerical model, wherein the calculation arrangement is configured tocalculate values of concentrations of human blood proteins as an outputof the simulation, and wherein the calculation arrangement is configuredto translate at least some of the calculated values of concentrations ofhuman blood proteins into a risk value, which risk value describes arisk of clotting and/or embolism and/or bleeding of the first bloodcirculation, further comprising a display arrangement configured todisplay the risk value.
 14. Program element for predicting anddisplaying a blood dilution risk value of a first blood circulation,which when being executed by a processor is adapted to carry out:receiving measured coagulation data describing a haemostatic situationof the first blood circulation at a first point in time (S1 a), applyingthe measured coagulation data as an input for a numerical model, thenumerical model being a mathematical and dynamical representation of ablood dilution of the first blood circulation (S2), performing asimulation of a time development of the haemostatic situation by meansof the numerical model and based on the measured coagulation data usedas an input for the numerical model (S3), calculating values ofconcentrations of human blood proteins as an output of the simulation(S4), and translating at least some of the calculated values ofconcentrations of human blood proteins into a risk value, which riskvalue describes a risk of clotting and/or embolism and/or bleeding forthe first blood circulation (S5).
 15. Computer readable medium in whicha program element for predicting and displaying a blood dilution riskvalue of a first blood circulation is stored, which, when being executedby a processor is adapted to carry out: receiving measured coagulationdata describing a haemostatic situation of the first blood circulationat a first point in time (S1 a), applying the measured coagulation dataas an input for a numerical model, the numerical model being amathematical and dynamical representation of a blood dilution of thefirst blood circulation (S2), performing a simulation of a timedevelopment of the haemostatic situation by means of the numerical modeland based on the measured coagulation data used as an input for thenumerical model (S3), calculating values of concentrations of humanblood proteins as an output of the simulation (S4), and translating atleast some of the calculated values of concentrations of human bloodproteins into a risk value, which risk value describes a risk ofclotting and/or embolism and/or bleeding for the first blood circulation(S5).